Will it be big tech companies like Google, Amazon, Apple, or Facebook, who will use AI to increase their power or wealth; or will the benefits be more evenly and democratically available?
According to Gartner, the global enterprise value derived from AI will total $1.2 trillion this year, a 70 percent increase from 2017. AI-derived business value is projected to reach up to $3.9 trillion by 2022.
I am telling you, the world’s first trillionaires are going to come from somebody who masters AI and all its derivatives and applies it in ways we never thought of.
~ Mark Cuban
Machine learning is really important and it will continue to improve but it is kind of done. The crazy new stuff is living on top of machine learning with decision-making systems which are now able to have models of the world through machine learning.
~ Dr. Andrew Moore
Dean of the School of Computer Science
At Carnegie Mellon University
PowerPoint slides from AI presentation
As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.
~ Judea Pearl
“Elon Musk doesn’t really deserve to have a voice in the public discourse about machine learning. He’s not an expert…”
Professor Zachary Lipton is an Assistant Professor in the Tepper School of Business at Carnegie Mellon University, with an appointment in the Machine Learning Department. He recently completed four years of PhD studies at UC San Diego’s Artificial Intelligence Group.
His research interests are eclectic, spanning both methods, applications, and social impacts of machine learning (ML), there exist a few notable clusters. He is especially interested in modeling temporal dynamics and sequential structure in healthcare data, e.g., Learning to Diagnose. Additionally, he works on critical questions related to how we use ML in the wild, yielding The Mythos of Model Interpretability, and more recent work on the desirability and reconcilability of various statistical interpretations of fairness.
He is a native of New Rochelle, New York, attended Columbia University as an undergraduate, and is a jazz saxophonist.
Terrance Jackson: What is the difference between artificial intelligence, machine learning, and deep learning?
Zachary Lipton: From the crazy way these topics are covered in the media, it can be hard to tell the meanings of the various terms. Often they are compared to each other, e.g. what deep learning can do vs what machine learning can do. The most faithful, simple way to put it is that they have a subset relationship. AI was a field long before people were interested in machine learning. It encompasses the study of how to do, with machines, all things that we think requires something like human intelligence. Of course that makes it a bit of a moving target. Once we know how to do something well, such as playing chess, then we sometimes don’t subsequently view it as a critical piece of AI.
Security tokens will dominate the blockchain universe!
Trevor Koverko is prominent blockchain founder, investor and speaker.
After launching his career at the convergence of Wall Street and Silicon Valley, Trevor became a very early leader in the blockchain community.
Trevor started in 2012 in Bitcoin, has keynoted major blockchain events like The North American Bitcoin Conference, and seeded foundation projects like Ethereum, Aion, QTum, Hive, EOS, and Shapeshift.
In 2017, after predicting the mega-trend of financial securities migrating to the blockchain, Trevor cofounded Polymath – the worlds largest securities token network.
Trevor graduated from Canada’s leading business school, Ivey, was a NHL draft pick of the New York Rangers and is a 4x attendee of Satoshi Roundtable.
Terrance Jackson: What is Polymath?
Trevor Koverko: I founded Polymath in 2017 after wanting to launch a token of my own for a company I founded.
I quickly learned that the token I wanted to launch would actually be considered a security token — a token that would represent shares in my company. I also learned that the barrier to entry when it came to creating a security token was simply too high for many companies.
That’s when I had the idea for Polymath and to disrupt the legacy securities industry. Polymath, which is an open-source platform, gives issuers of financial products access to the blockchain, smart contracts, and token creation technology.
Polymath provides a protocol to ease issuers– such as venture capital firms, investment funds, and companies– through the complex tech and legal processes of a successful security token launch.
In short, the idea behind Polymath is an interface between financial securities and the blockchain.
A Machine Learning Demonstration by
Monday, May 21 @ 7 pm
Larchmont Public Library
121 Larchmont Avenue, Larchmont, NY
Can we use artificial intelligence and machine learning techniques on information collected by companies such as Google, Facebook, Wikipedia, and Twitter to help predict financial markets?
Based on the research of Tobias Preis, a professor of Behavioural Science & Finance at the Warwick Business School, using a trading strategy based on the changes of how often people Googled the word “debt,” yielded a return of 326% for the Dow Jones Industrial Average (DJIA). This is compared to a 16% return for a buy and hold strategy.
We must educate our children for the 21st Century
When it comes to technology skills, the U.S. comes in last place — right below Poland. In addition, there was a significant racial difference with non-whites scoring below whites.
That’s why we are introducing students to artificial intelligence (A.I.), computer vision, data science, machine learning, robotics and blockchain technology.
Tech’s biggest companies are placing huge bets on artificial intelligence (A.I.) where typical A.I. specialists can be paid from $300,000 to $500,000 a year or more in salary and company stock.
The Achievement Gap
For decades, educators have struggled to close the “achievement gap,” the persistent differences in test scores, grades and graduation rates among students of different races, ethnicities and, in some subjects, genders.
According to an American Psychological Association article, a group of social and cognitive psychologists have approach this problem not based on the idea that at least some of these disparities are the result of faulty teaching or broken school systems, but instead spring from toxic stereotypes that cause ethnic-minority and other students such as women to question whether they belong in school and whether they can do well there. While such a major problem might seem to require widespread social change to fix, the psychologists are finding evidence that short, simple interventions can make a surprisingly large difference.
In a Scientific American article “Time to Raise the Profile of Women and Minorities in Science” written by Brian Welle and Megan Smith of Google, we learn:
Google recently commissioned a project to identify what makes girls pursue education in computer science. The findings reinforced what we already knew. Encouragement from a parent or teacher is essential for them to appreciate their own abilities. They need to understand the work itself and see its impact and importance. They need exposure to the field by having a chance to give it a shot. And, most important, they need to understand that opportunities await them in the technical industry.
It took some time, but Google realized that it recognized zero women with their Google Doodles, the embellishments of their corporate logo on their home page. Little things like this can have big impacts and to change the situation we need to look beyond the individual. As Malcolm Gladwell wrote in Outliers which The New York Times printed the first chapter:
[Y]ou couldn’t understand why someone was healthy if all you did was think about their individual choices or actions in isolation. You had to look beyond the individual. You had to understand what culture they were a part of, and who their friends and families were, and what town in Italy their family came from. You had to appreciate the idea that community — the values of the world we inhabit and the people we surround ourselves with — has a profound effect on who we are. The value of an outlier was that it forced you to look a little harder and dig little deeper than you normally would to make sense of the world. And if you did, you could learn something from the outlier that could use to help everyone else.In Outliers, I want to do for our understanding of success what Stewart Wolf did for our understanding of health.
Emin Gün Sirer is a computer science professor at Cornell University. His research spans operating systems, networking, and distributed systems. He’s also co-director of the Initiative for Cryptocurrencies & Contracts, which is an initiative of faculty members at Cornell University, Cornell Tech, UC Berkeley, UIUC and the Technion to help to advance the adoption of cryptocurrencies and smart contracts.
In 2002, he started Karma, an early cryptocurrency that was the first to utilize a proof-of-work concept. He has written several influential white papers and blog posts (on Hacking Distributed) that have altered the course of Ethereum’s development. He was among the first to warn about the vulnerabilities that led to the collapse of The DAO. He also acts as the Blockchain Advisor for the WeTrust project.
He is currently number 29 on the list of the Most Influential Blockchain People.
Terrance Jackson: In 2013, You and Ittay Eyal wrote “Bitcoin is Broken.” Is Bitcoin still broken?
Emin Gün Sirer: Indeed, we found the biggest known fundamental weakness in Satoshi Nakamoto’s consensus protocol, known as Selfish Mining. Using our strategy, one can subvert Satoshi’s protocol, and possibly make more money than their fair share, at the cost of disrupting the system’s behavior. Luckily, we provided a fix for selfish mining attacks for miners smaller than 25%, but the threat from large miners is always going to be present.
Now that the attack is well-known, the community knows how to detect such attacks and put pressure on the actors who launch them. In fact, if anything, the community is hyper-diligent against miners that are too big, and puts pressure on them to break them up. No Bitcoin miner is big enough to unilaterally go selfish and harm the system.
The situation is quite different in other cryptocurrencies, however. Selfish Mining could be employed against other smaller coins.